Dissection of the ATPase Active Site of P1 ParA Reveals Multiple Active Forms Essential for Plasmid Partition
Bibliographic record
Abstract
The segregation, or partition, of bacterial plasmids is driven by the action of plasmid-encoded partition ATPases, which work to position plasmids inside the cell. The most common type of partition ATPase, generally called ParA, is represented by the P1 plasmid ParA protein. ParA interacts with P1 ParB (the site-specific DNA binding protein that recognizes the parS partition site), and interacts with the bacterial chromosome via an ATP-dependent nonspecific DNA binding activity. ParA also regulates expression of the par genes by acting as a transcriptional repressor. ParA requires ATP for multiple steps and in different ways during the partition process. Here, we analyze the properties of mutations in P1 ParA that are altered in a key lysine in the Walker A motif of the ATP binding site. Four different residues at this position (Lys, Glu, Gln, Arg) result in four different phenotypes in vivo . We focus particularly on the arginine substitution (K122R) because it results in a worse-than-null and dominant-negative phenotype called ParPD. We show that ParAK122R binds and hydrolyzes ATP, although the latter activity is reduced compared with wild-type. ParAK122R interacts with ParB, but the consequences of the interaction are damaged. The ability of ParB to stimulate the ATPase activity of ParA in vitro and its repressor activity in vivo is defective. The K122R mutation specifically damages the disassembly of ParA-ParB-DNA partition complexes, which we believe explains the ParPD phenotype in vivo . Background: The ParA partition ATPase drives the dynamic positioning of plasmids in bacteria. Results: Different mutations in a critical lysine of the P1 ParA ATP binding site block different steps in partition. Conclusion: ATP induces multiple transitions in ParA that are necessary for partition. Significance: ParA-like proteins, which transport plasmids and other macromolecular cargo, likely use ATP in similar ways to promote localization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".